Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations201
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory196.8 KiB
Average record size in memory1002.5 B

Variable types

Numeric11
Text2
Categorical13

Alerts

aspiration is highly overall correlated with bore and 4 other fieldsHigh correlation
body-style is highly overall correlated with height and 1 other fieldsHigh correlation
bore is highly overall correlated with aspiration and 14 other fieldsHigh correlation
city-mpg is highly overall correlated with bore and 6 other fieldsHigh correlation
compression-ratio is highly overall correlated with aspiration and 6 other fieldsHigh correlation
curb-weight is highly overall correlated with city-mpg and 7 other fieldsHigh correlation
drive-wheels is highly overall correlated with make and 1 other fieldsHigh correlation
engine-location is highly overall correlated with bore and 5 other fieldsHigh correlation
engine-size is highly overall correlated with bore and 13 other fieldsHigh correlation
engine-type is highly overall correlated with bore and 5 other fieldsHigh correlation
fuel-system is highly overall correlated with aspiration and 5 other fieldsHigh correlation
fuel-type is highly overall correlated with bore and 4 other fieldsHigh correlation
height is highly overall correlated with body-style and 5 other fieldsHigh correlation
highway-mpg is highly overall correlated with city-mpg and 7 other fieldsHigh correlation
length is highly overall correlated with bore and 9 other fieldsHigh correlation
make is highly overall correlated with bore and 11 other fieldsHigh correlation
num-of-cylinders is highly overall correlated with bore and 7 other fieldsHigh correlation
num-of-doors is highly overall correlated with body-styleHigh correlation
peak-rpm is highly overall correlated with aspiration and 8 other fieldsHigh correlation
price is highly overall correlated with city-mpg and 6 other fieldsHigh correlation
stroke is highly overall correlated with aspiration and 15 other fieldsHigh correlation
symboling is highly overall correlated with height and 1 other fieldsHigh correlation
wheel-base is highly overall correlated with bore and 11 other fieldsHigh correlation
width is highly overall correlated with city-mpg and 9 other fieldsHigh correlation
fuel-type is highly imbalanced (53.3%) Imbalance
engine-location is highly imbalanced (88.8%) Imbalance
num-of-cylinders is highly imbalanced (58.6%) Imbalance
symboling has 65 (32.3%) zeros Zeros

Reproduction

Analysis started2025-02-01 22:52:51.864460
Analysis finished2025-02-01 22:53:12.256537
Duration20.39 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84079602
Minimum-2
Maximum3
Zeros65
Zeros (%)32.3%
Negative25
Negative (%)12.4%
Memory size1.7 KiB
2025-02-02T04:23:12.322085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2548017
Coefficient of variation (CV)1.4923973
Kurtosis-0.70717762
Mean0.84079602
Median Absolute Deviation (MAD)1
Skewness0.19737036
Sum169
Variance1.5745274
MonotonicityNot monotonic
2025-02-02T04:23:12.401078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 65
32.3%
1 52
25.9%
2 32
15.9%
3 27
13.4%
-1 22
 
10.9%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.9%
0 65
32.3%
1 52
25.9%
2 32
15.9%
3 27
13.4%
ValueCountFrequency (%)
3 27
13.4%
2 32
15.9%
1 52
25.9%
0 65
32.3%
-1 22
 
10.9%
-2 3
 
1.5%
Distinct52
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2025-02-02T04:23:12.560004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.3830846
Min length1

Characters and Unicode

Total characters479
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)5.0%

Sample

1st row?
2nd row?
3rd row?
4th row164
5th row164
ValueCountFrequency (%)
37
 
18.4%
161 11
 
5.5%
91 8
 
4.0%
150 7
 
3.5%
134 6
 
3.0%
128 6
 
3.0%
104 6
 
3.0%
74 5
 
2.5%
95 5
 
2.5%
103 5
 
2.5%
Other values (42) 105
52.2%
2025-02-02T04:23:12.798579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 151
31.5%
8 44
 
9.2%
5 38
 
7.9%
? 37
 
7.7%
9 36
 
7.5%
0 36
 
7.5%
4 36
 
7.5%
2 30
 
6.3%
6 29
 
6.1%
3 26
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 479
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 151
31.5%
8 44
 
9.2%
5 38
 
7.9%
? 37
 
7.7%
9 36
 
7.5%
0 36
 
7.5%
4 36
 
7.5%
2 30
 
6.3%
6 29
 
6.1%
3 26
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 479
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 151
31.5%
8 44
 
9.2%
5 38
 
7.9%
? 37
 
7.7%
9 36
 
7.5%
0 36
 
7.5%
4 36
 
7.5%
2 30
 
6.3%
6 29
 
6.1%
3 26
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 479
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 151
31.5%
8 44
 
9.2%
5 38
 
7.9%
? 37
 
7.7%
9 36
 
7.5%
0 36
 
7.5%
4 36
 
7.5%
2 30
 
6.3%
6 29
 
6.1%
3 26
 
5.4%

make
Categorical

High correlation 

Distinct22
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
108 

Length

Max length13
Median length11
Mean length6.5024876
Min length3

Characters and Unicode

Total characters1307
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.9%
nissan 18
 
9.0%
mazda 17
 
8.5%
mitsubishi 13
 
6.5%
honda 13
 
6.5%
volkswagen 12
 
6.0%
subaru 12
 
6.0%
peugot 11
 
5.5%
volvo 11
 
5.5%
dodge 9
 
4.5%
Other values (12) 53
26.4%

Length

2025-02-02T04:23:12.911216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.9%
nissan 18
 
9.0%
mazda 17
 
8.5%
mitsubishi 13
 
6.5%
honda 13
 
6.5%
volkswagen 12
 
6.0%
subaru 12
 
6.0%
peugot 11
 
5.5%
volvo 11
 
5.5%
dodge 9
 
4.5%
Other values (12) 53
26.4%

Most occurring characters

ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
n 71
 
5.4%
u 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
n 71
 
5.4%
u 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
n 71
 
5.4%
u 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 153
 
11.7%
o 151
 
11.6%
s 106
 
8.1%
t 100
 
7.7%
e 80
 
6.1%
n 71
 
5.4%
u 71
 
5.4%
i 65
 
5.0%
d 62
 
4.7%
m 57
 
4.4%
Other values (15) 391
29.9%

fuel-type
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
gas
181 
diesel
20 

Length

Max length6
Median length3
Mean length3.2985075
Min length3

Characters and Unicode

Total characters663
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 181
90.0%
diesel 20
 
10.0%

Length

2025-02-02T04:23:13.084443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:13.272233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gas 181
90.0%
diesel 20
 
10.0%

Most occurring characters

ValueCountFrequency (%)
s 201
30.3%
g 181
27.3%
a 181
27.3%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 201
30.3%
g 181
27.3%
a 181
27.3%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 201
30.3%
g 181
27.3%
a 181
27.3%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 201
30.3%
g 181
27.3%
a 181
27.3%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

aspiration
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
std
165 
turbo
36 

Length

Max length5
Median length3
Mean length3.358209
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 165
82.1%
turbo 36
 
17.9%

Length

2025-02-02T04:23:13.498959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:13.672646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
std 165
82.1%
turbo 36
 
17.9%

Most occurring characters

ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 201
29.8%
s 165
24.4%
d 165
24.4%
u 36
 
5.3%
r 36
 
5.3%
b 36
 
5.3%
o 36
 
5.3%

num-of-doors
Categorical

High correlation 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
four
113 
two
86 
?
 
2

Length

Max length4
Median length4
Mean length3.5422886
Min length1

Characters and Unicode

Total characters712
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 113
56.2%
two 86
42.8%
? 2
 
1.0%

Length

2025-02-02T04:23:13.862767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:14.061551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
four 113
56.2%
two 86
42.8%
2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
o 199
27.9%
f 113
15.9%
u 113
15.9%
r 113
15.9%
t 86
12.1%
w 86
12.1%
? 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 199
27.9%
f 113
15.9%
u 113
15.9%
r 113
15.9%
t 86
12.1%
w 86
12.1%
? 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 199
27.9%
f 113
15.9%
u 113
15.9%
r 113
15.9%
t 86
12.1%
w 86
12.1%
? 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 199
27.9%
f 113
15.9%
u 113
15.9%
r 113
15.9%
t 86
12.1%
w 86
12.1%
? 2
 
0.3%

body-style
Categorical

High correlation 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
sedan
94 
hatchback
68 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6119403
Min length5

Characters and Unicode

Total characters1329
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 94
46.8%
hatchback 68
33.8%
wagon 25
 
12.4%
hardtop 8
 
4.0%
convertible 6
 
3.0%

Length

2025-02-02T04:23:14.259038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:14.465003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan 94
46.8%
hatchback 68
33.8%
wagon 25
 
12.4%
hardtop 8
 
4.0%
convertible 6
 
3.0%

Most occurring characters

ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 263
19.8%
h 144
10.8%
c 142
10.7%
n 125
9.4%
e 106
8.0%
d 102
 
7.7%
s 94
 
7.1%
t 82
 
6.2%
b 74
 
5.6%
k 68
 
5.1%
Other values (8) 129
9.7%

drive-wheels
Categorical

High correlation 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
fwd
118 
rwd
75 
4wd
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters603
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 118
58.7%
rwd 75
37.3%
4wd 8
 
4.0%

Length

2025-02-02T04:23:14.686476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:14.829012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd 118
58.7%
rwd 75
37.3%
4wd 8
 
4.0%

Most occurring characters

ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 201
33.3%
d 201
33.3%
f 118
19.6%
r 75
 
12.4%
4 8
 
1.3%

engine-location
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
front
198 
rear
 
3

Length

Max length5
Median length5
Mean length4.9850746
Min length4

Characters and Unicode

Total characters1002
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 198
98.5%
rear 3
 
1.5%

Length

2025-02-02T04:23:15.018933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:15.162089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
front 198
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 204
20.4%
f 198
19.8%
o 198
19.8%
n 198
19.8%
t 198
19.8%
e 3
 
0.3%
a 3
 
0.3%

wheel-base
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.797015
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:15.352596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0663656
Coefficient of variation (CV)0.061402316
Kurtosis0.9484451
Mean98.797015
Median Absolute Deviation (MAD)2.8
Skewness1.0312614
Sum19858.2
Variance36.800791
MonotonicityNot monotonic
2025-02-02T04:23:15.662527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.7 20
 
10.0%
94.5 19
 
9.5%
95.7 13
 
6.5%
96.5 8
 
4.0%
97.3 7
 
3.5%
100.4 6
 
3.0%
107.9 6
 
3.0%
99.1 6
 
3.0%
98.8 6
 
3.0%
98.4 6
 
3.0%
Other values (42) 104
51.7%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.5%
93.3 1
 
0.5%
93.7 20
10.0%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.5%
108 1
 
0.5%
107.9 6
3.0%
106.7 1
 
0.5%

length
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.201
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:15.981803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.3
Q1166.8
median173.2
Q3183.5
95-th percentile197
Maximum208.1
Range67
Interquartile range (IQR)16.7

Descriptive statistics

Standard deviation12.322175
Coefficient of variation (CV)0.070735389
Kurtosis-0.065191628
Mean174.201
Median Absolute Deviation (MAD)6.9
Skewness0.15444635
Sum35014.4
Variance151.836
MonotonicityNot monotonic
2025-02-02T04:23:16.298771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.5%
188.8 11
 
5.5%
171.7 7
 
3.5%
186.7 7
 
3.5%
166.3 7
 
3.5%
165.3 6
 
3.0%
177.8 6
 
3.0%
176.2 6
 
3.0%
186.6 6
 
3.0%
172 5
 
2.5%
Other values (63) 125
62.2%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 1
 
0.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.5%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

width
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.889055
Minimum60.3
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:16.505659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.6
95-th percentile70.3
Maximum72
Range11.7
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.1014708
Coefficient of variation (CV)0.031894081
Kurtosis0.67865517
Mean65.889055
Median Absolute Deviation (MAD)1.4
Skewness0.87502904
Sum13243.7
Variance4.4161796
MonotonicityNot monotonic
2025-02-02T04:23:16.633332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
63.8 24
 
11.9%
66.5 23
 
11.4%
65.4 15
 
7.5%
68.4 10
 
5.0%
64.4 10
 
5.0%
63.6 9
 
4.5%
64 9
 
4.5%
65.5 8
 
4.0%
65.2 7
 
3.5%
65.6 6
 
3.0%
Other values (33) 80
39.8%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 9
 
4.5%
63.8 24
11.9%
63.9 3
 
1.5%
64 9
 
4.5%
64.1 2
 
1.0%
64.2 6
 
3.0%
ValueCountFrequency (%)
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%
68.8 1
 
0.5%

height
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.766667
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:16.906527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.4478222
Coefficient of variation (CV)0.045526761
Kurtosis-0.43290815
Mean53.766667
Median Absolute Deviation (MAD)1.6
Skewness0.029173299
Sum10807.1
Variance5.9918333
MonotonicityNot monotonic
2025-02-02T04:23:17.044859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
7.0%
55.7 12
 
6.0%
54.1 10
 
5.0%
54.5 10
 
5.0%
55.5 9
 
4.5%
52 9
 
4.5%
56.7 8
 
4.0%
54.3 8
 
4.0%
52.6 7
 
3.5%
56.1 7
 
3.5%
Other values (39) 107
53.2%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
3.0%
50.5 1
 
0.5%
50.6 5
 
2.5%
50.8 14
7.0%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
4.0%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.5%

curb-weight
Real number (ℝ)

High correlation 

Distinct169
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.6667
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:17.188013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1905
Q12169
median2414
Q32926
95-th percentile3505
Maximum4066
Range2578
Interquartile range (IQR)757

Descriptive statistics

Standard deviation517.29673
Coefficient of variation (CV)0.20241166
Kurtosis0.034915576
Mean2555.6667
Median Absolute Deviation (MAD)377
Skewness0.70580359
Sum513689
Variance267595.9
MonotonicityNot monotonic
2025-02-02T04:23:17.377670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2579 2
 
1.0%
2403 2
 
1.0%
2290 2
 
1.0%
2145 2
 
1.0%
2756 2
 
1.0%
3139 2
 
1.0%
Other values (159) 176
87.6%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 1
0.5%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

engine-type
Categorical

High correlation 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
ohc
145 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12

Length

Max length5
Median length3
Mean length3.119403
Min length1

Characters and Unicode

Total characters627
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 145
72.1%
ohcf 15
 
7.5%
ohcv 13
 
6.5%
dohc 12
 
6.0%
l 12
 
6.0%
rotor 4
 
2.0%

Length

2025-02-02T04:23:17.531856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:17.616384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ohc 145
72.1%
ohcf 15
 
7.5%
ohcv 13
 
6.5%
dohc 12
 
6.0%
l 12
 
6.0%
rotor 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 193
30.8%
h 185
29.5%
c 185
29.5%
f 15
 
2.4%
v 13
 
2.1%
d 12
 
1.9%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

num-of-cylinders
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
four
157 
six
24 
five
 
10
two
 
4
eight
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.8955224
Min length3

Characters and Unicode

Total characters783
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 157
78.1%
six 24
 
11.9%
five 10
 
5.0%
two 4
 
2.0%
eight 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2025-02-02T04:23:17.742889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:17.838760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
four 157
78.1%
six 24
 
11.9%
five 10
 
5.0%
two 4
 
2.0%
eight 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 167
21.3%
o 161
20.6%
r 158
20.2%
u 157
20.1%
i 38
 
4.9%
s 24
 
3.1%
x 24
 
3.1%
e 18
 
2.3%
v 11
 
1.4%
t 10
 
1.3%
Other values (4) 15
 
1.9%

engine-size
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.87562
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:17.949883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q198
median120
Q3141
95-th percentile194
Maximum326
Range265
Interquartile range (IQR)43

Descriptive statistics

Standard deviation41.546834
Coefficient of variation (CV)0.32746113
Kurtosis5.4974908
Mean126.87562
Median Absolute Deviation (MAD)22
Skewness1.9791442
Sum25502
Variance1726.1395
MonotonicityNot monotonic
2025-02-02T04:23:18.093107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
122 15
 
7.5%
92 15
 
7.5%
98 14
 
7.0%
97 14
 
7.0%
108 13
 
6.5%
110 12
 
6.0%
90 10
 
5.0%
109 8
 
4.0%
141 7
 
3.5%
120 7
 
3.5%
Other values (33) 86
42.8%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 10
5.0%
91 5
 
2.5%
92 15
7.5%
97 14
7.0%
98 14
7.0%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
194 3
1.5%
183 4
2.0%
181 6
3.0%
173 1
 
0.5%

fuel-system
Categorical

High correlation 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
mpfi
92 
2bbl
64 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.8955224
Min length3

Characters and Unicode

Total characters783
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 92
45.8%
2bbl 64
31.8%
idi 20
 
10.0%
1bbl 11
 
5.5%
spdi 9
 
4.5%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2025-02-02T04:23:18.222448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T04:23:18.315810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 92
45.8%
2bbl 64
31.8%
idi 20
 
10.0%
1bbl 11
 
5.5%
spdi 9
 
4.5%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 156
19.9%
i 143
18.3%
p 102
13.0%
f 94
12.0%
m 93
11.9%
l 78
10.0%
2 64
8.2%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

bore
Categorical

High correlation 

Distinct39
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
3.62
23 
3.19
20 
3.15
15 
2.97
 
12
3.03
 
10
Other values (34)
121 

Length

Max length4
Median length4
Mean length3.9402985
Min length1

Characters and Unicode

Total characters792
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)5.5%

Sample

1st row3.47
2nd row3.47
3rd row2.68
4th row3.19
5th row3.19

Common Values

ValueCountFrequency (%)
3.62 23
 
11.4%
3.19 20
 
10.0%
3.15 15
 
7.5%
2.97 12
 
6.0%
3.03 10
 
5.0%
3.46 9
 
4.5%
3.31 8
 
4.0%
3.78 8
 
4.0%
3.43 8
 
4.0%
2.91 7
 
3.5%
Other values (29) 81
40.3%

Length

2025-02-02T04:23:18.442505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.62 23
 
11.4%
3.19 20
 
10.0%
3.15 15
 
7.5%
2.97 12
 
6.0%
3.03 10
 
5.0%
3.46 9
 
4.5%
3.31 8
 
4.0%
3.78 8
 
4.0%
3.43 8
 
4.0%
3.27 7
 
3.5%
Other values (29) 81
40.3%

Most occurring characters

ValueCountFrequency (%)
3 218
27.5%
. 197
24.9%
1 60
 
7.6%
2 56
 
7.1%
9 52
 
6.6%
5 43
 
5.4%
7 41
 
5.2%
6 38
 
4.8%
4 33
 
4.2%
0 32
 
4.0%
Other values (2) 22
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 218
27.5%
. 197
24.9%
1 60
 
7.6%
2 56
 
7.1%
9 52
 
6.6%
5 43
 
5.4%
7 41
 
5.2%
6 38
 
4.8%
4 33
 
4.2%
0 32
 
4.0%
Other values (2) 22
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 218
27.5%
. 197
24.9%
1 60
 
7.6%
2 56
 
7.1%
9 52
 
6.6%
5 43
 
5.4%
7 41
 
5.2%
6 38
 
4.8%
4 33
 
4.2%
0 32
 
4.0%
Other values (2) 22
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 218
27.5%
. 197
24.9%
1 60
 
7.6%
2 56
 
7.1%
9 52
 
6.6%
5 43
 
5.4%
7 41
 
5.2%
6 38
 
4.8%
4 33
 
4.2%
0 32
 
4.0%
Other values (2) 22
 
2.8%

stroke
Categorical

High correlation 

Distinct37
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
3.40
19 
3.03
14 
3.23
14 
3.15
14 
3.39
13 
Other values (32)
127 

Length

Max length4
Median length4
Mean length3.9402985
Min length1

Characters and Unicode

Total characters792
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)3.5%

Sample

1st row2.68
2nd row2.68
3rd row3.47
4th row3.40
5th row3.40

Common Values

ValueCountFrequency (%)
3.40 19
 
9.5%
3.03 14
 
7.0%
3.23 14
 
7.0%
3.15 14
 
7.0%
3.39 13
 
6.5%
2.64 11
 
5.5%
3.29 9
 
4.5%
3.35 9
 
4.5%
3.46 8
 
4.0%
3.19 6
 
3.0%
Other values (27) 84
41.8%

Length

2025-02-02T04:23:18.553260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3.40 19
 
9.5%
3.15 14
 
7.0%
3.03 14
 
7.0%
3.23 14
 
7.0%
3.39 13
 
6.5%
2.64 11
 
5.5%
3.29 9
 
4.5%
3.35 9
 
4.5%
3.46 8
 
4.0%
3.41 6
 
3.0%
Other values (27) 84
41.8%

Most occurring characters

ValueCountFrequency (%)
3 222
28.0%
. 197
24.9%
2 60
 
7.6%
4 59
 
7.4%
0 58
 
7.3%
5 44
 
5.6%
1 41
 
5.2%
9 36
 
4.5%
6 33
 
4.2%
7 21
 
2.7%
Other values (2) 21
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 222
28.0%
. 197
24.9%
2 60
 
7.6%
4 59
 
7.4%
0 58
 
7.3%
5 44
 
5.6%
1 41
 
5.2%
9 36
 
4.5%
6 33
 
4.2%
7 21
 
2.7%
Other values (2) 21
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 222
28.0%
. 197
24.9%
2 60
 
7.6%
4 59
 
7.4%
0 58
 
7.3%
5 44
 
5.6%
1 41
 
5.2%
9 36
 
4.5%
6 33
 
4.2%
7 21
 
2.7%
Other values (2) 21
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 222
28.0%
. 197
24.9%
2 60
 
7.6%
4 59
 
7.4%
0 58
 
7.3%
5 44
 
5.6%
1 41
 
5.2%
9 36
 
4.5%
6 33
 
4.2%
7 21
 
2.7%
Other values (2) 21
 
2.7%

compression-ratio
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.164279
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:18.648308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.9
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.0049655
Coefficient of variation (CV)0.39402358
Kurtosis5.0688725
Mean10.164279
Median Absolute Deviation (MAD)0.4
Skewness2.5844624
Sum2043.02
Variance16.039749
MonotonicityNot monotonic
2025-02-02T04:23:18.886403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.9%
9.4 26
12.9%
8.5 14
 
7.0%
9.5 13
 
6.5%
9.3 11
 
5.5%
8.7 9
 
4.5%
8 8
 
4.0%
9.2 8
 
4.0%
7 6
 
3.0%
8.6 5
 
2.5%
Other values (22) 55
27.4%
ValueCountFrequency (%)
7 6
3.0%
7.5 5
 
2.5%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
4.0%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.5%
8.5 14
7.0%
ValueCountFrequency (%)
23 5
2.5%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.5%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 2
 
1.0%
Distinct59
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2025-02-02T04:23:19.235400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.4477612
Min length1

Characters and Unicode

Total characters492
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)9.5%

Sample

1st row111
2nd row111
3rd row154
4th row102
5th row115
ValueCountFrequency (%)
68 19
 
9.5%
69 10
 
5.0%
70 9
 
4.5%
116 9
 
4.5%
110 8
 
4.0%
95 7
 
3.5%
101 6
 
3.0%
114 6
 
3.0%
62 6
 
3.0%
88 6
 
3.0%
Other values (49) 115
57.2%
2025-02-02T04:23:19.828180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 132
26.8%
6 72
14.6%
8 56
11.4%
0 49
 
10.0%
2 48
 
9.8%
5 35
 
7.1%
9 31
 
6.3%
7 30
 
6.1%
4 27
 
5.5%
3 10
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 132
26.8%
6 72
14.6%
8 56
11.4%
0 49
 
10.0%
2 48
 
9.8%
5 35
 
7.1%
9 31
 
6.3%
7 30
 
6.1%
4 27
 
5.5%
3 10
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 132
26.8%
6 72
14.6%
8 56
11.4%
0 49
 
10.0%
2 48
 
9.8%
5 35
 
7.1%
9 31
 
6.3%
7 30
 
6.1%
4 27
 
5.5%
3 10
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 132
26.8%
6 72
14.6%
8 56
11.4%
0 49
 
10.0%
2 48
 
9.8%
5 35
 
7.1%
9 31
 
6.3%
7 30
 
6.1%
4 27
 
5.5%
3 10
 
2.0%

peak-rpm
Categorical

High correlation 

Distinct23
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
5500
36 
4800
36 
5000
27 
5200
23 
5400
11 
Other values (18)
68 

Length

Max length4
Median length4
Mean length3.9701493
Min length1

Characters and Unicode

Total characters798
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)2.0%

Sample

1st row5000
2nd row5000
3rd row5000
4th row5500
5th row5500

Common Values

ValueCountFrequency (%)
5500 36
17.9%
4800 36
17.9%
5000 27
13.4%
5200 23
11.4%
5400 11
 
5.5%
6000 9
 
4.5%
5800 7
 
3.5%
5250 7
 
3.5%
4500 7
 
3.5%
4150 5
 
2.5%
Other values (13) 33
16.4%

Length

2025-02-02T04:23:20.028471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5500 36
17.9%
4800 36
17.9%
5000 27
13.4%
5200 23
11.4%
5400 11
 
5.5%
6000 9
 
4.5%
5800 7
 
3.5%
5250 7
 
3.5%
4500 7
 
3.5%
4150 5
 
2.5%
Other values (13) 33
16.4%

Most occurring characters

ValueCountFrequency (%)
0 410
51.4%
5 186
23.3%
4 83
 
10.4%
8 43
 
5.4%
2 38
 
4.8%
6 15
 
1.9%
1 8
 
1.0%
3 5
 
0.6%
7 4
 
0.5%
9 4
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 410
51.4%
5 186
23.3%
4 83
 
10.4%
8 43
 
5.4%
2 38
 
4.8%
6 15
 
1.9%
1 8
 
1.0%
3 5
 
0.6%
7 4
 
0.5%
9 4
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 410
51.4%
5 186
23.3%
4 83
 
10.4%
8 43
 
5.4%
2 38
 
4.8%
6 15
 
1.9%
1 8
 
1.0%
3 5
 
0.6%
7 4
 
0.5%
9 4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 410
51.4%
5 186
23.3%
4 83
 
10.4%
8 43
 
5.4%
2 38
 
4.8%
6 15
 
1.9%
1 8
 
1.0%
3 5
 
0.6%
7 4
 
0.5%
9 4
 
0.5%

city-mpg
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.179104
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:20.250746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.4232205
Coefficient of variation (CV)0.25510123
Kurtosis0.75396809
Mean25.179104
Median Absolute Deviation (MAD)5
Skewness0.68043347
Sum5061
Variance41.257761
MonotonicityNot monotonic
2025-02-02T04:23:20.524699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.9%
19 27
13.4%
24 22
10.9%
27 14
 
7.0%
26 12
 
6.0%
17 12
 
6.0%
23 12
 
6.0%
21 8
 
4.0%
25 8
 
4.0%
30 8
 
4.0%
Other values (19) 50
24.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 5
 
2.5%
17 12
6.0%
18 3
 
1.5%
19 27
13.4%
20 3
 
1.5%
21 8
 
4.0%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 5
2.5%
37 6
3.0%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highway-mpg
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.686567
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:20.963815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8151499
Coefficient of variation (CV)0.22208903
Kurtosis0.56117114
Mean30.686567
Median Absolute Deviation (MAD)5
Skewness0.54950715
Sum6168
Variance46.446269
MonotonicityNot monotonic
2025-02-02T04:23:21.075183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.5%
38 17
 
8.5%
24 17
 
8.5%
30 16
 
8.0%
32 16
 
8.0%
34 14
 
7.0%
37 13
 
6.5%
28 12
 
6.0%
29 10
 
5.0%
33 9
 
4.5%
Other values (20) 58
28.9%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 7
 
3.5%
23 7
 
3.5%
24 17
8.5%
25 19
9.5%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 2
 
1.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.5%

price
Real number (ℝ)

High correlation 

Distinct186
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13207.129
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-02-02T04:23:21.218529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6189
Q17775
median10295
Q316500
95-th percentile32528
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation7947.0663
Coefficient of variation (CV)0.60172549
Kurtosis3.2315369
Mean13207.129
Median Absolute Deviation (MAD)3306
Skewness1.8096753
Sum2654633
Variance63155863
MonotonicityNot monotonic
2025-02-02T04:23:21.360547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
18150 2
 
1.0%
8845 2
 
1.0%
8495 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
7957 2
 
1.0%
7775 2
 
1.0%
5572 2
 
1.0%
Other values (176) 181
90.0%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2025-02-02T04:23:09.961846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:53.500728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:54.597396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:56.786429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:58.145734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.398753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:00.606271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:02.825331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:04.116699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:05.432924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:07.472150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:10.087945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:53.580224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:54.692477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.008233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:58.260041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.494457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:00.796079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.068620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:04.225690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:05.560399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:07.703316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:10.214916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:53.675776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:54.803656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.165258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:58.385226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.590116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:01.017846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.173314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:04.333929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:05.674122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:07.925114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:10.386458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:53.770576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:55.010007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.274063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:58.494505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.685439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:01.223954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.284123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:04.426937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:05.808878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:08.178765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:10.555766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:53.866351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:55.200714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.369226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:58.588112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.789091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:01.398359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.380184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:04.522246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:05.956950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:08.384930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:10.689997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:53.946026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:55.417951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.480657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:58.700647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.907739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:01.636140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.491628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:04.633284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:06.071934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:08.636173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:10.825088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:54.047093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:55.660216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.576151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:58.921116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:00.018281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:01.840149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.587052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:04.744232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:06.195250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:08.844860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:10.990669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:54.136239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:55.902080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.672526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.016685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:00.114172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:02.086421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.692091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:04.931938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:06.432940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:09.098307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:11.133624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:54.231922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:56.103741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.781750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.112301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:00.225545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:02.285387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.790615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:05.062958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:06.671239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:09.368704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:11.292609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:54.327838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:56.310543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:57.920295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.207322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:00.320518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:02.476107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.889886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:05.163760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:07.037057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:09.615026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:11.435517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:54.501747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:56.548218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:58.035006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:22:59.302847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:00.416080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:02.682303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:03.998204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:05.290060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:07.242709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T04:23:09.804221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-02T04:23:21.487041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
aspirationbody-styleborecity-mpgcompression-ratiocurb-weightdrive-wheelsengine-locationengine-sizeengine-typefuel-systemfuel-typeheighthighway-mpglengthmakenum-of-cylindersnum-of-doorspeak-rpmpricestrokesymbolingwheel-basewidth
aspiration1.0000.0000.6000.2110.5510.3720.0920.0000.2720.1650.6170.3810.2540.3070.2180.4090.1530.0130.5180.3940.5150.1870.3160.320
body-style0.0001.0000.3430.0000.0530.2440.2300.4370.2150.1400.1540.1740.5010.0000.2550.3310.1100.5270.1870.2400.3940.3370.3390.147
bore0.6000.3431.0000.5030.6410.4710.4750.9020.6950.7440.6900.7410.5540.4570.5190.6260.5530.1420.6040.4510.7530.4420.5900.480
city-mpg0.2110.0000.5031.0000.476-0.8060.3810.112-0.7220.2450.3040.403-0.0800.969-0.6610.3600.4470.0000.358-0.8310.424-0.022-0.484-0.673
compression-ratio0.5510.0530.6410.4761.000-0.2130.1050.000-0.2330.3440.5180.9920.0080.434-0.1800.4890.5250.1520.612-0.1780.7820.021-0.120-0.139
curb-weight0.3720.2440.471-0.806-0.2131.0000.4520.0940.8740.3380.2910.3280.363-0.8310.8900.4960.5000.1660.3800.9140.512-0.2610.7650.859
drive-wheels0.0920.2300.4750.3810.1050.4521.0000.1250.4670.4400.3860.0850.3810.4240.4100.6050.3140.0560.3850.4430.5440.2650.4120.411
engine-location0.0000.4370.9020.1120.0000.0940.1251.0000.6740.4050.0000.0000.3040.0950.0000.8030.2870.1020.9460.4680.9080.2700.5680.078
engine-size0.2720.2150.695-0.722-0.2330.8740.4670.6741.0000.5590.3300.1510.209-0.7170.7800.5240.6640.0620.5740.8280.749-0.1820.6460.763
engine-type0.1650.1400.7440.2450.3440.3380.4400.4050.5591.0000.4190.2580.4310.3670.3510.6750.5770.0990.5480.2610.8330.2290.3920.415
fuel-system0.6170.1540.6900.3040.5180.2910.3860.0000.3300.4191.0000.9850.2950.3440.3220.5410.3720.1500.4030.2870.6090.2680.2230.259
fuel-type0.3810.1740.7410.4030.9920.3280.0850.0000.1510.2580.9851.0000.2720.3590.1020.3650.1740.1980.7560.3400.6430.2190.3390.267
height0.2540.5010.554-0.0800.0080.3630.3810.3040.2090.4310.2950.2721.000-0.1380.5320.4840.3540.3600.3440.2640.576-0.5300.6410.371
highway-mpg0.3070.0000.4570.9690.434-0.8310.4240.095-0.7170.3670.3440.359-0.1381.000-0.6890.4010.5210.1310.370-0.8270.4470.050-0.531-0.692
length0.2180.2550.519-0.661-0.1800.8900.4100.0000.7800.3510.3220.1020.532-0.6891.0000.5010.3710.2360.3670.8100.468-0.4040.9130.890
make0.4090.3310.6260.3600.4890.4960.6050.8030.5240.6750.5410.3650.4840.4010.5011.0000.5490.1780.5330.3700.7320.4550.5130.553
num-of-cylinders0.1530.1100.5530.4470.5250.5000.3140.2870.6640.5770.3720.1740.3540.5210.3710.5491.0000.0000.4950.4470.6820.1620.3380.565
num-of-doors0.0130.5270.1420.0000.1520.1660.0560.1020.0620.0990.1500.1980.3600.1310.2360.1780.0001.0000.4730.0000.2200.4790.2900.126
peak-rpm0.5180.1870.6040.3580.6120.3800.3850.9460.5740.5480.4030.7560.3440.3700.3670.5330.4950.4731.0000.4140.7210.3820.4210.404
price0.3940.2400.451-0.831-0.1780.9140.4430.4680.8280.2610.2870.3400.264-0.8270.8100.3700.4470.0000.4141.0000.425-0.1430.6820.812
stroke0.5150.3940.7530.4240.7820.5120.5440.9080.7490.8330.6090.6430.5760.4470.4680.7320.6820.2200.7210.4251.0000.4990.5360.509
symboling0.1870.3370.442-0.0220.021-0.2610.2650.270-0.1820.2290.2680.219-0.5300.050-0.4040.4550.1620.4790.382-0.1430.4991.000-0.542-0.261
wheel-base0.3160.3390.590-0.484-0.1200.7650.4120.5680.6460.3920.2230.3390.641-0.5310.9130.5130.3380.2900.4210.6820.536-0.5421.0000.816
width0.3200.1470.480-0.673-0.1390.8590.4110.0780.7630.4150.2590.2670.371-0.6920.8900.5530.5650.1260.4040.8120.509-0.2610.8161.000

Missing values

2025-02-02T04:23:11.873495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-02T04:23:12.106599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03?alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495
13?alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500
21?alfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500
32164audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950
42164audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450
52?audigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250
61158audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710
71?audigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920
81158audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875
92192bmwgasstdtwosedanrwdfront101.2176.864.854.32395ohcfour108mpfi3.502.808.81015800232916430
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
191-174volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415
192-2103volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985
193-174volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515
194-2103volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420
195-174volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950
196-195volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845
197-195volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045
198-195volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485
199-195volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470
200-195volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625